
Class-agnostic attention learning with clustering loss: Nguyen et al in background modeling [1]} propose a method to separate foreground and background using a clustering loss by penalizing the discriminative capacity of background features. Class-agnostic foreground and background features \(f\)  and \(b\)  (def REF ) are encouraged to be distinct using a clustering loss:
\(z_f = \frac{\exp (u f)}{\exp (u f)+\exp (v f)} \ , \ z_b = \frac{\exp (v b)}{\exp (u b)+\exp (v b)},\) 
\(\mathcal {L}_{cluster} = - \log {z_f}- \log {z_b},\) 
